Attributed roadway trajectories for self-driving vehicles

ABSTRACT

An attributed roadway trajectory comprises at least one ordered series of attributed roadway trajectory points, which are spaced along a curve that is defined in a terrestrial coordinate frame and tracks an along-roadway physical feature of a real-world roadway segment portrayed in a point cloud. Any arbitrary attributed roadway trajectory point on the curve passes a predetermined proximity test for proximity to point cloud points discriminated as representing part of the along-roadway physical feature. Each attributed roadway trajectory point has at least one attribute value representing a characteristic of the real-world roadway segment at a position on the real-world roadway segment spatially associated with the attributed roadway trajectory point. A control system of a self-driving road vehicle can use the attribute value(s) to adjust control of the self-driving road vehicle based on sensor-independent roadway data (e.g. where the position of the attributed roadway trajectory point(s) remains outside of sensor range).

TECHNICAL FIELD

The present disclosure relates to roadway models, and more particularlyto generation of attributed roadway trajectories for self-driving andassisted-driving road vehicles.

BACKGROUND

Self-driving road vehicles can travel autonomously on a roadway withouthuman intervention, and can at least autonomously maintain lane/roadposition while avoiding collisions. In some cases, a self-driving roadvehicle can also independently navigate along a series of roadways froman initial position to a destination without human intervention; thesetypes of self-driving road vehicles are referred to as“automatic-driving road vehicles”. In other cases, referred to as“assisted-driving road vehicles”, although the self-driving road vehiclecan autonomously maintain lane/road position while avoiding collisions,and may also perform some additional driving tasks autonomously, thenavigation tasks must be performed by a human operator. Assisted-drivingvehicles may be considered to have a more sophisticated form of cruisecontrol. For example, an assisted-driving road vehicle could maintain aconstant speed (subject to speed reduction for collision avoidance)within a given highway lane indefinitely, but it would be up to a humanoperator to take manual control of the vehicle and navigate off thehighway at the appropriate exit. The term “self-driving road vehicle”,as used herein, refers to road vehicles that can at least autonomouslymaintain lane/road position while avoiding collisions, and encompassesboth assisted-driving road vehicles and automatic-driving road vehicles.

Self-driving road vehicles rely on an array of sensors and a roadwaymodel representing features of the roadway on which the road vehicle istravelling. The roadway model is derived from survey data of theroadways (e.g., point clouds, geo-referenced images) acquired on anearlier date. The control system, typically incorporating an onboardcomputer, uses the sensors to obtain data about the environment. Usefulinformation is then extracted from these sensor data by computinghardware and software. The information obtained from the sensors canthen be used in conjunction with the roadway model to perform navigationor other autonomous driving functions, including directing the roadvehicle along the roadway toward a destination, compliance with trafficsignals, speed limits and other legal requirements, and avoidingcollisions with pedestrians and other vehicles.

There is a limit to the information that can be extracted from datacaptured by on-board vehicle sensors. Some relevant information sourcesmay be outside the field of view of those sensors, some information(e.g. legal information) may not be amenable to extraction from senseddata, and there may be practical limits on the amount of sensor datathat can be captured and processed in real time. While some of theseissues can be obviated with more sophisticated and powerful sensors andadditional processing capability, this does not completely resolve theissues and also increases vehicle cost and complexity

SUMMARY

The present disclosure describes roadway models for use withself-driving road vehicles, as well as exemplary methods for generatingthese roadway models. The roadway models described herein may be used byself-driving road vehicles as a source of information about the roadwaybeing driven that is outside the range of the sensors carried by theroad vehicle, cannot be detected by those sensors, or cannot beextracted in real time from the data acquired by those sensors. Theroadway models described herein may be used in conjunction withconventional navigation and mapping technology used by self-driving roadvehicles.

In one aspect, the present disclosure is directed to a method forpre-processing roadway data. The method comprises receiving, in aprocessor of a data processing system, at least one ordered series ofroadway trajectory points. For each ordered series of roadway trajectorypoints, the roadway trajectory points are spaced along a curve that isdefined in a terrestrial coordinate frame and tracks an along-roadwayphysical feature of a real-world roadway segment portrayed in a pointcloud, and each roadway trajectory point has a position in theterrestrial coordinate frame. For any arbitrary roadway trajectory pointon the curve, that roadway trajectory point passes a predeterminedproximity test for proximity to point cloud points discriminated asrepresenting part of the along-roadway physical feature. Each roadwaytrajectory point has at least one roadway attribute. For each roadwaytrajectory point in the ordered series of roadway trajectory points, theprocessor evaluates at least one predefined characteristic of thereal-world roadway segment at a position on the real-world roadwaysegment that is spatially associated with the roadway trajectory pointto generate an attribute value for each predefined characteristic, andassigns each attribute value to the corresponding roadway attribute ofthe roadway trajectory point. This method generates an attributedroadway trajectory for use in a control system of a self-driving roadvehicle.

In a preferred embodiment, the roadway trajectory points are uniformlyspaced along the curve.

In some embodiments, the at least one ordered series of roadwaytrajectory points comprises a plurality of ordered series of roadwaytrajectory points. Boundaries between the ordered series of roadwaytrajectory points are defined by discontinuities between the curve of apreceding one of the series of roadway trajectory points and the curveof a subsequent one of the series of roadway points, and thediscontinuities between the curves are associated with respectivecorresponding discontinuities in the along-roadway physical feature.

The roadway attribute(s) may comprise at least one non-geometric roadwayattribute. In such embodiments, evaluating the predefinedcharacteristic(s) of the real-world roadway segment and assigning eachattribute value to the corresponding roadway attribute of the roadwaytrajectory point may comprise the use of landmark points. In oneimplementation, the processor receives a set of landmark points, witheach landmark point having a position in the terrestrial coordinateframe and having at least one landmark attribute characterizing thatlandmark point. For each landmark point, the processor locates a closestroadway trajectory point that is horizontally closest to that landmarkpoint and tests a horizontal distance between that landmark point andthe closest roadway trajectory point against a maximum distancethreshold. Responsive to the horizontal distance being less than orequal to the maximum distance threshold, the processor assigns at leastone attribute value to a corresponding at least one roadway attribute ofthe closest roadway trajectory point according to at least onecorresponding landmark attribute.

The set of landmark points may comprise a plurality of landmark pointsof different landmark types.

The roadway attribute(s) may comprise at least one non-geometric roadwayattribute and/or at least one geometric roadway attribute, and thegeometric roadway attribute(s) may comprise at least one road surfacegeometric roadway attribute and/or at least one trajectory curvegeometric roadway attribute. The road surface geometric roadwayattribute(s) may comprise at least one of road surface along-trajectoryslope and cross-trajectory slope, and the trajectory curve geometricroadway attribute(s) may comprise at least one of heading, horizontalcurvature, and vertical curvature.

In another aspect, the present disclosure is directed to a self-drivingroad vehicle. The self-driving road vehicle comprises a body, alocomotion system coupled to the body for accelerating, propelling anddecelerating the vehicle along a roadway, a steering system coupled tothe body for steering the vehicle, a sensor array carried by the bodyfor sensing driving data, the sensor array having a sensor range, and acontrol system carried by the body. The control system is coupled to thesensor array for receiving sensed driving data from the sensor array,the control system is coupled to the locomotion system for controllingthe locomotion system, and the control system is also coupled to thesteering system for controlling the steering system. A data storagemodule is coupled to and accessible by the control system; the datastorage module stores at least one attributed roadway trajectory. Eachattributed roadway trajectory comprises at least one ordered series ofattributed roadway trajectory points. For each ordered series ofattributed roadway trajectory points, the attributed roadway trajectorypoints are spaced along a curve defined in a terrestrial coordinateframe and tracking an along-roadway physical feature of a real-worldroadway segment portrayed in a point cloud. For any arbitrary attributedroadway trajectory point on the curve, that attributed roadwaytrajectory point passes a predetermined proximity test for proximity topoint cloud points discriminated as representing part of thealong-roadway physical feature. Each attributed roadway trajectory pointhas, in addition to its position in the terrestrial coordinate frame, atleast one roadway attribute having a respective attribute value. Eachattribute value represents a characteristic of the real-world roadwaysegment at a position on the real-world roadway segment that isspatially associated with the attributed roadway trajectory point. Thecontrol system is configured to obtain, for at least one of theattributed roadway trajectory points, at least one respective attributevalue representing sensor-independent roadway data and to use theattribute value(s) to adjust control of the locomotion system and/or thesteering system.

In certain preferred embodiments, the control system is configured toobtain respective attribute value(s) representing sensor-independentroadway data while the position in the terrestrial coordinate frame ofthe respective attributed roadway trajectory point(s) corresponds to aterrestrial position outside of the sensor range of the sensor array. Insuch embodiments, the control system may be further configured to usethe attribute value(s) to adjust control of the locomotion system and/orthe steering system while the position in the terrestrial coordinateframe of the respective attributed roadway trajectory point(s)corresponds to a terrestrial position outside of the sensor range of thesensor array.

In a further aspect, the present disclosure is directed to a method forcontrolling a self-driving road vehicle. According to the method, acontrol system of the self-driving road vehicle accesses an attributedroadway trajectory. The attributed roadway trajectory comprises at leastone ordered series of attributed roadway trajectory points. For eachordered series of attributed roadway trajectory points, the attributedroadway trajectory points are spaced along a curve defined in aterrestrial coordinate frame and tracking an along-roadway physicalfeature of a real-world roadway segment portrayed in a point cloud. Forany arbitrary attributed roadway trajectory point on the curve, thatattributed roadway trajectory point passes a predetermined proximitytest for proximity to point cloud points discriminated as representingpart of the along-roadway physical feature. Each attributed roadwaytrajectory point has, in addition to its position in the terrestrialcoordinate frame, at least one roadway attribute having a respectiveattribute value. Each attribute value represents a characteristic of thereal-world roadway segment at a position on the real-world roadwaysegment that is spatially associated with the attributed roadwaytrajectory point. The control system of the self-driving road vehicleobtains, for at least one of the attributed roadway trajectory points,at least one respective attribute value representing sensor-independentroadway data, and the control system of the self-driving road vehicleuses the attribute value(s) to adjust control of the locomotion systemand/or the steering system.

In certain preferred embodiments, the control system of the self-drivingroad vehicle obtains the respective attribute value(s) representingsensor-independent roadway data while the position in the terrestrialcoordinate frame of the respective attributed roadway trajectorypoint(s) corresponds to a terrestrial position outside of the sensorrange of the sensor array. In these embodiments, the control system mayuse the attribute value(s) to adjust control of the locomotion systemand/or the steering system while the position in the terrestrialcoordinate frame of the respective attributed roadway trajectorypoint(s) corresponds to a terrestrial position outside of the sensorrange of the sensor array.

In some embodiments, for both a self-driving road vehicle and a methodfor controlling a self-driving road vehicle, there are a plurality ofordered series of attributed roadway trajectory points. Boundariesbetween the ordered series of attributed roadway trajectory points maybe defined by discontinuities between the curve of a preceding one ofthe series of attributed roadway trajectory points and the curve of asubsequent one of the series of roadway points, with the discontinuitiesbetween the curves being associated with respective correspondingdiscontinuities in the along-roadway physical feature.

The attributed roadway trajectory points may be uniformly spaced alongthe curve.

The roadway attribute(s) may comprise at least one non-geometric roadwayattribute and/or at least one geometric roadway attribute, and thegeometric roadway attribute(s) may comprise at least one road surfacegeometric roadway attribute and/or at least one trajectory curvegeometric roadway attribute.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features will become more apparent from the followingdescription in which reference is made to the appended drawings wherein:

FIG. 1 is a high-level flow chart showing an exemplary method forgenerating an attributed roadway trajectory;

FIG. 2 shows an exemplary point cloud display for a roadway area;

FIG. 3 is a schematic illustration of a method for estimatingperpendicular distance between a test point on an interpolating curveand a point cloud point discriminated as sampling an along-roadwayphysical feature;

FIG. 4 is an exemplary display of point cloud points together with atube whose central axis is an interpolating curve and whose radius is adistance threshold;

FIG. 5 shows an example of a point cloud sampling a roadway in whichthere is a discontinuity between a first portion of the i^(th) lanedivider marking and a second portion of the i^(th) lane divider markingbecause a new lane has been added to the right edge of the road;

FIG. 6 is a flow chart showing an exemplary method for evaluating one ormore predefined characteristics of a roadway to obtain attribute valuesand using the attribute values to populate roadway attributes of roadwaytrajectory points in an interpolated point sequence;

FIG. 7 is an exemplary flow chart showing population of three exemplarytypes of roadway attribute;

FIG. 8 is a flow chart showing an exemplary method for evaluatingnon-geometric roadway attributes;

FIG. 9 is a flow chart showing an exemplary method for populatingtrajectory curve geometric roadway attributes;

FIG. 10 schematically illustrates an exemplary height filteringtechnique

FIG. 11 is a flow chart showing an exemplary method for populating roadsurface geometric roadway attributes;

FIG. 12 is a schematic illustration of an exemplary self-driving roadvehicle using an attributed roadway trajectory;

FIG. 13 is a flow chart showing an exemplary method for controlling aself-driving road vehicle; and

FIG. 14 is a schematic illustration of an exemplary computer systemwhich can be used in implementing aspects of the present technology.

DETAILED DESCRIPTION

The type of roadway model described herein is referred to as an“attributed roadway trajectory”. The term “attributed roadwaytrajectory”, as used herein, refers to a sequence of three-dimensionalcoordinates in a terrestrial coordinate frame, each of which describes aterrestrial position and height and also has a set of one or moreroadway attributes having respective attribute values. The coordinatesmay be represented in a number of ways. For example, sphericalcoordinates may be used (e.g. latitude/longitude/height), or (x,y,z)values in a Cartesian terrestrial coordinate frame may be used. Theattribute values are derived from legal, physical and navigationalproperties of the roadway and objects that exist along the roadway, asdescribed in more detail below. A single coordinate triplet in anattributed roadway trajectory, together with its attribute value(s), isreferred to herein as an “attributed roadway trajectory point”.

The attributed roadway trajectory points in the attributed roadwaytrajectory generally follow the roadway being modeled. In preferredembodiments, the attributed roadway trajectory points in the attributedroadway trajectory sample the location of an along-roadway physicalfeature such as a road edge, a lane divider paint marking, or a roadshoulder paint marking. In other embodiments, the trajectory points maysample, for example, a calculated midpoint of a lane. The sequence ofthe attributed roadway trajectory points matches the driving directionof the roadway that includes the physical feature—the second point inthe sequence spatially follows the first in the driving direction, thethird point in the sequence spatially follows the second in drivingdirection, and so on. The attributed roadway trajectory points arespaced, preferably uniformly, along a mathematically defined curve inthree dimensions that intersects the coordinate triplets of theattributed roadway trajectory points.

Reference is now made to FIG. 1, which is a high-level flow chartshowing an exemplary method 100 for generating an attributed roadwaytrajectory. At step 102, the method 100 receives external data samplinga roadway surface, and at step 104 the method 100 generates an initialpoint sequence, typically non-uniformly spaced, from the external data;this initial point sequence is used to generate an interpolating curveand the points in that initial point sequence are referred to herein as“sample points” since they are the samples used to generate theinterpolating curve. At step 106, the method 100 generates aninterpolated point sequence on the interpolating curve; thus, theinterpolated point sequence is generated from the initial pointsequence. The points in the interpolated point sequence are referred toherein as “roadway trajectory points”. At step 108 the method 100evaluates one or more predefined characteristics of the roadwayassociated with the positions of the roadway trajectory points in theinterpolated point sequence and assigns one or more attribute values tocorresponding roadway attributes for the respective roadway trajectorypoints. The result of step 108 is an interpolated point sequence ofroadway trajectory points each having one or more roadway attributeswith assigned attribute values, in other words, an attributed roadwaytrajectory. Each of steps 102 to 108 is described in greater detailbelow.

The geometric calculations performed at each of steps 102 to 108 arepreferably performed in a locally level Cartesian coordinate frame. Theterm “locally level” refers to a Cartesian coordinate frame whose (x, y)plane, at origin, is tangent to an ellipsoid representing the Earth'ssurface and whose positive y-axis points true north at the origin andwhose positive z-axis points away from the Earth's notional center. Assuch, as used herein, the terms “horizontal”, “horizontally”,“horizontal distance” and related terms refer to measurement in the (x,y) plane of a the locally level Cartesian coordinate frame, and the term“vertical” refers to the direction of the z-axis.

It is to be appreciated that the point cloud points, landmark points (asdescribed further below) sample points, roadway trajectory points andattributed roadway trajectory points (along with certain roadwayattributes) may be represented in some other terrestrial coordinateframe other than a locally level Cartesian coordinate frame. In suchembodiments they may transformed into the relevant locally levelCartesian coordinate frame for the purpose of geometric calculation andthe results of the geometric calculations may be transformed back to theoriginal terrestrial coordinate frame. While one skilled in the artcould equivalently perform the geometric calculations directly withinthe original terrestrial coordinate frame, such calculations wouldimplicitly include transformation to and from a locally level Cartesiancoordinate frame and it is therefore considered more straightforward totransform the coordinates and perform the calculations aftertransformation into the locally level Cartesian coordinate frame.

The external data received at step 102 generally includes point cloudsfor the relevant roadway area. A point cloud for a roadway area is a setof regularly or non-regularly distributed three-dimensional coordinatesin a terrestrial coordinate frame that sample the roadway surface andsurfaces adjacent to the roadway. When the terrestrial coordinate frameis Cartesian, each coordinate in the point cloud will be represented byan (x,y,z) coordinate triplet. Each coordinate in the point cloud has anintensity value; the intensity value for a coordinate in the point cloudis related to the light reflectance in a specific wavelength interval ofthe surface that is sampled by that coordinate. The coordinates in apoint cloud, together with their intensity values, are referred toherein as “point cloud points”. A point cloud may be displayed withhardware and software to provide monochrome renderings of views of theroadway and its surroundings. FIG. 2 shows an exemplary point cloud fora roadway area.

Normally, the point clouds are generated from data acquired with a LightDetection and Ranging (LIDAR) system designed for outdoor surveyingapplications and operated from a road vehicle or aircraft. Multiple suchsystems are available from multiple vendors, along with software thattransforms the raw data acquired by the system into point clouds. Thesystem/software is chosen such that the density of the point cloud andits intensity values will support feature tracking with the necessaryaccuracy. The density of the point cloud and its intensity values mustreveal the along-roadway physical feature that is to be tracked by theattributed roadway trajectory (e.g., a lane divider paint marking, roadedge, road shoulder paint marking). In addition, the density of thepoint cloud and its intensity values, and the accuracy with which thesamples are referenced to the terrestrial coordinate frame (i.e., thegeo-referencing accuracy of the samples) must be fine enough to supportthe production of an attributed roadway trajectory that has thegeo-referencing accuracy required by its intended application. In onepreferred self-driving road vehicle embodiment, for example, lanecentering operations may require that the coordinates in the terrestrialcoordinate frame for each trajectory point be within 10 centimeters ofthe true global position.

Referring again to FIG. 1, step 104 generates a sequence ofthree-dimensional terrestrial coordinates that sample the position ofthe chosen along-roadway physical feature (e.g., a lane divider paintmarking, road edge, road shoulder paint marking), as portrayed in one ormore input point clouds received at step 102, these coordinates arereferred to as “sample points”. The sample points in the sequencegenerated at step 104 are similar to the attributed roadway trajectorypoints that will result from step 108, in that they are defined in thesame coordinate frame, represent the same along-roadway physicalfeature, and are sequenced to match the driving direction. The samplepoints in the sequence generated at step 104 are different from theattributed roadway trajectory points in that (i) they are typically morewidely spaced than the attributed roadway trajectory points; and (ii)they do not have attribute values that are derived from properties ofthe roadway and objects that exist along the roadway. As indicatedabove, the output of step 104 is referred to as an “initial pointsequence”.

The initial point sequence may be generated with one of many differentprocedures; in certain currently preferred embodiments, step 104involves human interaction with a software tool although automation ofthis step 104 is also contemplated. The feasibility of automationdepends on the along-roadway physical feature and how it is portrayed inthe point cloud. Certain procedures for determining the acceptability ofan initial point sequence will now be described.

In general, the sample points are derived from one or more point cloudsthat portray the stretch of roadway and the along-roadway physicalfeature (e.g., a lane divider paint marking, road edge, road shoulderpaint marking), that is of interest. A single sample point in theinitial point sequence may have the same coordinates as a single pointcloud point, or be calculated from the coordinates of a neighbourhood ofpoint cloud points. In either case, the sample points must be sequencedto match the driving direction, and the distribution of sample pointsmust be such that an interpolating curve generated from the initialpoint sequence is acceptable input data for step 106, as describedbelow.

The initial point sequence is used to generate a curve that interpolatesthe initial point sequence and is intended to track the along-roadwayphysical feature; this curve is referred to as an “interpolating curve”.It will be appreciated that where the along-roadway physical featuregenerally follows a straight line (e.g. over a long, straight stretch ofhighway), the curve will also follow a straight line; in this context astraight line is merely a special case of a curve in which the curvatureis zero.

The interpolating curve will be used in step 106 and therefore step 104includes an iterative process of acceptance testing and adjustment forthe interpolating curve using a distance threshold. Test points on theinterpolating curve must have, except where the test point is opposite agap in the along-roadway physical feature (e.g. spaces betweenindividual markings in a dashed lane marker), a perpendicular distanceto the along-roadway physical feature that is no greater than aspecified distance threshold. Thus, for each test point on theinterpolating curve, that test point either (a) passes a predeterminedproximity test for proximity to point cloud points that have beendiscriminated as representing part of the along-roadway physicalfeature, or (b) is associated with a gap in the along-roadway physicalfeature. Preferably, the test points are uniformly spaced.

If the point cloud points that comprise the along-roadway physicalfeature can be automatically extracted and if gaps in the along-roadwayphysical feature can be automatically localized, then the acceptabilityof the interpolating curve may be automatically determined by computingan estimate of the perpendicular distance to the along-roadway physicalfeature at each test point (i.e. each “step” increment in the case ofuniformly spaced test points) along the interpolating curve that is notopposite a gap in the along-roadway physical feature. Where theinterpolated point sequence resulting from step 106 (described below)will be uniformly spaced, the step value is the specified uniform curvelength between adjacent uniformly spaced points that will be used instep 106. Only if none of these estimated perpendicular distances aregreater than the threshold is the interpolating curve acceptable. If theinterpolating curve is not acceptable, then sample points must beremoved and/or added to the initial point sequence in the vicinity ofinterpolating curve locations at which the distance threshold isexceeded, and the interpolating curve recalculated, until theinterpolating curve becomes acceptable.

Referring now to FIG. 3, an estimate of the perpendicular distance Dbetween a test point P on the interpolating curve 202 and a point cloudpoint (e.g. point cloud point 204) discriminated as sampling thealong-roadway physical feature is calculated as follows. A notionalplane 206 is defined that is normal to the interpolating curve 202 atthat test point P (i.e. normal to the tangent 208 at that test point P).The procedure then finds all point cloud points (e.g. point cloud point204) that are discriminated as sampling the along-roadway physicalfeature and have a perpendicular distance d to the plane of no more thanhalf the distance threshold. If there are no such point cloud points,then the location violates the distance threshold. If there are suchpoint cloud points, then only if at least one of them has a distance Don the plane 206 to the test point P on the interpolating curve 202 thatis no greater than the distance threshold does the interpolating curve202 not violate the distance threshold at test point P.

If the point cloud points that comprise the along-roadway physicalfeature cannot be automatically extracted, then the acceptability of theinterpolating curve may be determined by manually inspecting a displayof the point cloud points together with a tube whose central axis is theinterpolating curve and whose radius is the distance threshold, as shownin FIG. 4. Point cloud points that discriminate part of thealong-roadway physical feature 400 are displayed brightly because of theintensity value associated with them (in this case these point cloudpoints discriminate a dashed lane marker). The tube 402 contains thepoint cloud points that discriminate the along-roadway physical feature,and a sphere 404 is rendered for each sample point such that the samplepoint is at the center of the sphere 404. The interpolating curve isonly acceptable if the point cloud points sampling the along-roadwayphysical feature intersect the surface of the tube or are inside thetube everywhere except where there are gaps in the along-roadwayphysical feature; this is shown to be the case in FIG. 4 for part of adashed lane marker along roadway physical feature. If the interpolatingcurve is not acceptable, then sample points must be removed or added tothe initial point sequence in the vicinity of interpolating curvelocations at which the distance threshold is exceeded until theinterpolating curve becomes acceptable. The marking of the locations ofthe sample points by way of a sphere 404 and the availability ofappropriate editing operations in the display software assists thisprocess.

An along-roadway physical feature that is sampled by an initial pointsequence may be discontinuous. For example, if the along-roadwayphysical feature is the i^(th) lane divider marking from the right edgeof the road, then where a new lane is added to the right edge of theroad (relative to the driving direction), the i^(th) lane dividermarking becomes the (i+1)^(th) lane divider marking. At that location,there is a discontinuity and the initial point sequence must start tosample the lane divider marking to the right. FIG. 5 shows an example ofa point cloud sampling a roadway 500 in which there is a discontinuity502 between a first portion 504 of the i^(th) lane divider marking and asecond portion 506 of the i^(th) lane divider marking because a new lane508 has been added to the right edge of the road. In the presentembodiment, it is considered advantageous to treat a discontinuousalong-roadway physical feature as a single logical entity whileaccounting for discontinuities. This is accomplished by dividing theinitial point sequence into “sub-runs”, in which the discontinuitiesform boundaries between sub-runs. All sample points between twodiscontinuities, or between a discontinuity and the start or end of theinitial point sequence, belong to the same sub-run. All sample pointshave a sub-run attribute value (an integer type) such that all samplepoints in the same sub-run have the same sub-run attribute value and thelast point in one sub-run and the first point in the next have differentsub-run attribute values. In the initial point sequence acceptabilitytest, the interpolating curve is not made to interpolate between pointsthat are on a discontinuity, instead, when a discontinuity isidentified, a new sub-run begins, with a new interpolating curve.Accordingly, in one embodiment step 104 (FIG. 1) interpolates eachsub-run of sample points of the initial point sequence with a parametriccurve, whose parameter is arc length, and each sub-run has its owninterpolating curve.

This sub-run structure will be inherited from the initial point sequenceby the interpolated point sequence generated at step 106, and theattributed roadway trajectory generated at step 108 will in turn inheritthe sub-run structure from the interpolated point sequence. Thus, theinterpolated point sequence may be made up of a series of sub-runs, andhence the attributed roadway trajectory may also be made up of a seriesof sub-runs. Each sub-run is an ordered series of roadway trajectorypoints, and therefore an interpolated point sequence that comprises aseries of sub-runs comprises a plurality of ordered series of roadwaytrajectory points, each spaced along a respective curve. The boundariesbetween the ordered series of roadway trajectory points (sub-runs) thatmake up the interpolated point sequence are defined by discontinuitiesbetween the curve of a preceding one of the series of roadway trajectorypoints and the curve of a subsequent one of the series of roadwaypoints; the discontinuities between the curves are associated withrespective corresponding discontinuities in the along-roadway physicalfeature.

Alternatively, each sub-run of a larger interpolated point sequence orattributed roadway trajectory may be characterized as a separate,smaller interpolated point sequence or attributed roadway trajectory,respectively.

The curve function is chosen such that between the sample points in theinitial point sequence, the curve will closely track the along-roadwayphysical feature that is sampled by those points. In the exemplaryembodiment, this is supported by application of the distance threshold(either automatically or manually) as described above in respect of step104. Where the along-roadway physical roadway feature is a lane divideror shoulder marking, an interpolating parametric cubic spline thatdefines a two- or three-dimensional curve (matching the dimensionalityof the sample points in the initial point sequence) is suitable.

In a preferred embodiment, if the terrestrial coordinate frame of theinitial point sequence has spherical coordinates (i.e., degrees latitudeand longitude) for horizontal position, these spherical coordinates aretransformed to coordinates in a Cartesian coordinate frame, and theinterpolation and generation of the roadway trajectory points that formthe interpolated point sequence are performed on the Cartesiancoordinates. The coordinates of the roadway trajectory points in theinterpolated point sequence may then be transformed to coordinates inthe original terrestrial coordinate frame (e.g. spherical coordinates).

At step 106, the interpolating curve generated and validated at step 104is used to calculate the three-dimensional coordinates of points on thatcurve. Preferably, the points are uniformly spaced, i.e. spaced at auniform interval along the curve. The driving-direction sequence ofthese coordinates is the interpolated point sequence, and (as notedabove) the individual points in the interpolated point sequence arereferred to as “roadway trajectory points”. The interpolated pointsequence inherits the sub-run structure and hence the interpolatingcurve does not interpolate between sub-runs.

In one embodiment where the roadway trajectory points are uniformlyspaced, the interpolating curve (x,y,z)=c(t) is defined such that c(0)is the position of the first sample point in the sub-run of an initialpoint sequence, m is the total curve length between the first and lastsample points in the sub-run, and step is the specified uniform curvelength between adjacent uniformly-spaced points. The first sample pointin the sub-run will become first roadway trajectory point in theinterpolated point sequence for that sub-run. The sequence ofuniformly-spaced coordinates is calculated as follows:

t = 0; i = 0; while (t < m) {    i^(th) coordinate pair (or triplet) =c(t);    t = t + step;    i= i+1; }

Let n be the total curve length between the first and last roadwaytrajectory points in the interpolated point sequence for that sub-run.Then, 0<=m−n<step. Thus, the total curve length for the interpolatedpoint sequence for a given sub-run will typically be shorter than thecurve length for the initial point sequence for that sub-run by lessthan the step amount. One meter (1 m) has been empirically found to be asuitable step length where the along-roadway physical feature is a lanedivider marking, shoulder marking or roadway edge.

As noted above, at step 108, the method 100 evaluates one or morepredefined characteristics of the roadway to obtain attribute values andassigns the calculated attribute values to the roadway attributes of theroadway trajectory points in the interpolated point sequence. Step 108will now be described with greater detail in the context of FIG. 6,which is a flow chart showing an exemplary method 600 for generating anattributed roadway trajectory. Thus, the flow chart showing the method600 is an elaboration of step 108. The method 600 is executed in aprocessor of a data processing system, and may be considered a methodfor pre-processing roadway data.

At step 602, the processor receives the interpolated point sequence ofroadway trajectory points generated at step 106 of the method 100 inFIG. 1. As noted above, the interpolated point sequence will comprise atleast one ordered series of roadway trajectory points, each having atleast one roadway attribute. If the interpolated point sequence is madeup of a series of sub-runs, there will be a plurality of ordered seriesof roadway trajectory points, each spaced along a respective curve, withboundaries between the ordered series of roadway trajectory points(sub-runs) defined by discontinuities between adjacent curves associatedwith respective corresponding discontinuities in the along-roadwayphysical feature. If each sub-run is treated as an independentinterpolated point sequence, there will be a single ordered series ofroadway trajectory points spaced along a single curve. In either case,as a result of steps 102 to 106, the curve(s) will be defined in aterrestrial coordinate frame and will track an along-roadway physicalfeature of a real-world roadway segment portrayed in a point cloud, andeach roadway trajectory point will have a position in the terrestrialcoordinate frame. Moreover, because of the acceptance testing applied atstep 104, for any arbitrary roadway trajectory point on the curve, thatroadway trajectory point passes a predetermined proximity test forproximity to the point cloud points that were discriminated asrepresenting part of the along-roadway physical feature. Preferably, theroadway trajectory points are uniformly spaced along the respectivecurve(s).

At step 604, the processor populates the roadway attributes for eachroadway trajectory point with attribute values. For each roadwaytrajectory point in the interpolated point sequence, the processorevaluates at least one predefined characteristic of the real-worldroadway segment at a position on the real-world roadway segment that isspatially associated with the roadway trajectory point. This evaluationgenerates an attribute value for each predefined characteristic, and theprocessor then assigns each attribute value to the corresponding roadwayattribute of the respective roadway trajectory point. Once all attributevalues have been assigned to the corresponding roadway attributes of therespective roadway trajectory points in the interpolated point sequence,the result is an attributed roadway trajectory. As will be explainedfurther below, the attributed roadway trajectory is used by a controlsystem of a self-driving road vehicle.

The roadway trajectory points may have various different types ofroadway attribute, and hence various different types of predefinedcharacteristics may be evaluated at step 604 to populate these roadwayattributes. The roadway attributes may include non-geometric roadwayattributes and geometric roadway attributes. FIG. 7 is a flow chartshowing an exemplary elaboration of step 604 which delineates populationof three exemplary types of roadway attribute.

At step 702, the processor populates non-geometric roadway attributes.The term “non-geometric”, as used herein, refers to roadway attributesthat are not related to the shape of the curve that was used tointerpolate sample points in the initial point sequence or to the shapeof the roadway surface. The roadway attributes populated at step 702capture non-geometric information about the roadway (e.g., the shouldersurface material at different locations, the locations of traffic signsof different types). At step 704A the processor populates trajectorycurve geometric roadway attributes and at step 704B the processorpopulates road surface geometric roadway attributes. Steps 702 and thecombination of steps 704A and step 704B may be performed in anysequence, or substantially simultaneously, but in preferred embodimentsstep 704A is performed before step 704B and the output of step 704A isused as the input to step 704B.

Step 702 is now described in more detail.

Step 702 is performed on a landmark point set, that is, a set oflandmark points. Landmark points are three-dimensional coordinates thatare in the same terrestrial coordinate frame as the interpolated pointsequence and which have one or more landmark attributes characterizingthe landmark attribute. Each landmark point represents an instance of aparticular landmark type. All landmark points of the same landmark typehave the same landmark attributes. In some cases, a particular landmarktype may not have any delineated landmark attributes; in these cases thelandmark type itself can be considered an implicit landmark attribute(i.e. presence/absence of the landmark). The landmark points of aparticular type comprise a set rather than a sequence (i.e., the orderof the points is not significant). The set of landmark types depends onthe specific intended application of the attributed trajectory. The setof landmark points for a particular landmark type is used to populate aparticular set of non-geometric roadway attributes of the interpolatedpoint sequence.

A landmark type represents either discrete objects that exist on or nextto the roadway (object landmarks) or changes to continuous roadwayproperties (property-change landmarks). Examples of property-changelandmarks are speed limit changes and changes in the drivability of theleft shoulder. Object landmarks are not related to a continuous roadwayproperty other than the absence or existence of an object. Examples ofobject landmarks are exit ramp starts, entrance ramp endings, and signs.

The set of points for a property-change landmark type are associatedwith an initial value that is the value of the roadway attribute derivedfrom that landmark type for the first point in the interpolated pointsequence. A property-change landmark point is placed along the normal ofthe interpolating curve at a point on the curve where the property isconsidered to change, within a specified horizontal threshold distancefrom that point on the curve.

The set of landmark points of a particular type may be generatedautomatically or with some degree of manual intervention, depending onthe type of landmark point. In any case, this is done with a softwareprogram. Landmark points may be derived from the point cloud(s) used togenerate the initial point sequence, and geo-referenced image or videodata may also be used in the landmark point selection; for example, ifnot all of the landmark types are clearly portrayed in the pointcloud(s).

FIG. 8 is a flow chart showing an exemplary elaboration of step 702,illustrating an exemplary method 800 for evaluating non-geometricroadway attributes; as noted above the method 800 will be implemented bya processor of a data processing system. At step 802, the processorreceives a set of landmark points, which may comprise a plurality oflandmark points of different landmark types. Each of the landmark pointshas a position in the same terrestrial coordinate frame as the roadwaytrajectory points. At step 804, the processor indexes to the next (orfirst) landmark type. In the illustrated embodiment, there are two broadcategories of landmark types: object landmarks and property-changelandmarks. Within these broad categories there are particular landmarktypes.

The following are examples of object landmark types:

-   -   highway entrance apices    -   highway exit apices    -   lane merge apices    -   lane split apices    -   lane divider markers with marker type    -   shoulder markers    -   traffic signs with sign type and position    -   traffic signals with signal type and position    -   symbols or text painted in lane with the meaning of the symbol        or text    -   medians

The following are examples of property-change landmark types:

-   -   road name change    -   road type change    -   road edge type change    -   shoulder drivability change    -   shoulder surface change    -   speed limit change

The indexing order at step 804 is not particularly important as long asthe method 800 eventually indexes through all object landmark types andall property-change landmark types. At step 806, the processor indexesto the next (or first) landmark point within the current landmark type.

Steps 808 and 810 determine associations between landmark points androadway trajectory points. Associations between landmark points androadway trajectory points are determined by finding the roadwaytrajectory point that is horizontally closest to a particular landmarkpoint and applying a maximum distance threshold. A landmark point isassociated with the roadway trajectory point in the interpolated pointsequence to which the landmark point has the minimum horizontal distanceif that minimum horizontal distance is no greater than a specifieddistance threshold. Thus, at step 808, the processor locates the roadwaytrajectory point that is horizontally closest to the current landmarkpoint, and at step 810 the processor tests whether the horizontaldistance between the current landmark point and the closest roadwaytrajectory point is less than or equal to the specified maximum distancethreshold. Responsive to a “yes” determination at step 810, theprocessor proceeds to step 812. A “yes” determination at step 810 meansthat an association has been established between the current landmarkpoint and the horizontally closest roadway trajectory point, and theprocessor proceeds to step 812 to assign attribute value(s) to thecorresponding roadway attribute(s) of the horizontally closest roadwaytrajectory point according to the corresponding landmark attribute(s).It will be appreciated that the association test (steps 808 and 810) mayequivalently be applied by testing whether the horizontal distance isless than (rather than less than or equal to) a maximum distancethreshold; this is simply a function of setting the threshold value.

A “no” at step 810 means there is no roadway trajectory point in theinterpolated point sequence for which the horizontal distance betweenthe landmark point and the roadway trajectory point is less than orequal to the specified distance threshold (since the closest roadwaytrajectory point is further from the landmark point than the specifieddistance threshold). This means that the current landmark point is notassociated with any roadway trajectory points in the interpolated pointsequence, and the processor proceeds to step 814 to check if there aremore landmark points of the current landmark type. If there are morelandmark points of the current landmark type (a “yes” at step 814), theprocessor returns to step 806 to index to the next landmark point of thecurrent landmark type. If there are no more landmark points of thecurrent landmark type (a “no” at step 814), the processor proceeds tostep 816 to check if there are more landmark types to evaluate. If thereare more landmark types (a “yes” at step 816), the processor returns tostep 804 to index to the next landmark type. If there are no morelandmark types (a “no” at step 816), this means that all landmark pointsof all landmark types have been evaluated, and the method 800 ends.

For each type of object landmark, the attributed roadway trajectorypoints will have at least one corresponding roadway attribute. Thatroadway attribute is valued (e.g., 0 or 1, true or false)—one valuemeans “an instance of the object is present” and the other means “noinstance of the object is present.” For any roadway trajectory point inthe interpolated point sequence, that roadway attribute is set to the“an instance of the landmark is present” value if that point isassociated with at least one landmark point of that type; otherwise, itis set to the “no instance of the object is present” value.

For an object landmark, there may be additional landmark attributes forwhich the roadway trajectory points have corresponding roadwayattributes. Each landmark point of that type has values for thoselandmark attributes, and those values are copied over to the roadwayattribute with which it is associated. Those roadway attributes are setto the null value for all roadway trajectory points that are notassociated with at least one landmark point of that type; they may beinitialized at the null value and changed only if an association isestablished at steps 808 and 810.

Each property-change landmark type has one attribute value, which is avalue of the roadway property that it represents. The attributed roadwaytrajectory points have the same roadway attribute (one roadway attributefor each property-change landmark type).

For each property-change landmark, the corresponding roadway attributeis set to the landmark type's initial value for each roadway trajectorypoint from (and including) the first roadway trajectory point in theinterpolated point sequence to (but not including) the first roadwaytrajectory point that is associated with a landmark point of that type.The roadway trajectory point that is associated with the landmark pointis assigned the attribute value of the landmark point, as are allfollowing roadway trajectory points up to (but not including) the nextroadway trajectory point that is associated with a landmark point ofthat type (or through to the end of the interpolated point sequence ifthere are no more point sequence points associated with a landmark pointof that type). Each subsequent landmark point/roadway trajectory pointassociation changes the attribute value in this manner.

The exemplary method 800 illustrates a logic flow in which the attributevalues are assigned to each of the respective roadway trajectory pointsimmediately following determination that a particular roadway trajectorypoint is associated with a particular landmark point. Other logic flowsare also contemplated. For example, the logic flow may first determineall associations between roadway trajectory points and landmark pointsand, once all of the associations between the roadway trajectory pointsand the landmark points have been established, attribute values may thenbe assigned to the roadway trajectory points based on the associations.

When a roadway attribute point is associated with multiple objectlandmarks points of the same type, then roadway attributes of thesequence point are populated only from the object landmark point of thattype to which it has the shortest horizontal distance—the other objectlandmark points of the same type are ignored. In rare cases, twolandmark points of the same type may be associated with the same roadwaytrajectory point. For example, there may be a very short unpaved stretchon an otherwise paved stretch of shoulder, or there may be multiplepainted symbols in close proximity. Special handling protocols can bedeployed in such cases. For example, the protocols may depend on thetype of landmark point, and may be automated or may flag the issue forhuman analysis and intervention.

Reference is now made to FIG. 9, which is a flow chart showing anexemplary method 900 that elaborates step 704A. As noted above, at step704A, the processor populates trajectory curve geometric roadwayattributes, that is, attributes of the interpolating curve on which theroadway trajectory points lie. Trajectory-curve geometric roadwayattributes for a roadway trajectory point are evaluated from the curveat the location where it intersects the roadway trajectory point. Themost important trajectory-curve geometric roadway attributes are headingand horizontal curvature; trajectory vertical curvature may also beevaluated.

If the terrestrial coordinate frame of the interpolated point sequencehas spherical coordinates (i.e., degrees latitude and longitude) forhorizontal position, then before commencing the method 900, thecoordinates of the interpolated point sequence are transformed tocoordinates in a locally level Cartesian coordinate frame that capturesthe interpolated point sequence. As indicated previously, the method 900is carried out by a processor of a data processing system.

At step 902, the processor evaluates the heading of the interpolatingcurve at each roadway trajectory point and at step 904 the processorassigns the resulting attribute values to the corresponding roadwayattributes. One exemplary procedure for evaluating the heading will nowbe described.

The heading roadway attribute value for a position t along theinterpolating curve, heading(t), is the angle in degrees between thepositive y-axis and the projection of the interpolating curve tangentvector at position t onto the xy-plane, measured clockwise with thepositive y-axis pointing at “12” and the positive x-axis pointing at“3”, adjusted for divergence as explained below. This will result in theheading value conforming to navigational convention. One exemplarymethod for calculating heading(t) is described below.

Let:

-   -   dx(t), dy(t), and dz(t) be the first derivatives of the        interpolating curve with respect to position along the curve at        position t;    -   atan2(x, y) be a function that returns the angle in radians from        the x-axis of a plane and the point given by the coordinates        (x, y) on it. The angle is positive for counter-clockwise angles        (upper half-plane, y>0), and negative for clockwise angles        (lower half-plane, y<0);    -   rad_to_deg=180.0/pi, the factor that converts radians to        degrees; and    -   grid_heading(t) be the angle in degrees between the positive        y-axis and the projection of the interpolating curve tangent        vector at position t onto the xy-plane, measured clockwise with        the positive y-axis pointing at “12” and the positive x-axis        pointing at “3”.        Then, grid_heading(t)=atan2(dx(t), dy(t))*rad_to_deg.

Note that grid_heading(t) only points exactly north if t is such thatx(t)=0, y(t)=0. Let divergence(x, y) be the heading of a horizontalvector at (x, y) that points in the same direction as the y-axis. Then,heading(t)=grid_heading(t)+divergence(x(t), y(t)).

A divergence(x, y) function for any ellipsoid may be based on thefollowing procedure:

-   -   1. Transform the (x, y) coordinates into (longitude, latitude)        coordinates for the ellipsoid.    -   2. Increment the latitude coordinate by a small amount (e.g.,        0.25 degrees) to get latitude′.    -   3. Transform (longitude, latitude′) to (x′, y′) in the Cartesian        coordinate frame.    -   4. Return −atan2(x, y)*rad_to_deg.

Divergence may be calculated in other ways as well.

At step 906, the processor evaluates the horizontal curvature of theinterpolating curve at each roadway trajectory point and at step 908 theprocessor assigns the resulting attribute values to the correspondingroadway attributes. One exemplary procedure for evaluating thehorizontal curvature will now be described.

The horizontal curvature for a position t along the interpolating curve,hcurvature(t), is the curvature of the projection of the interpolatingcurve on the xy-plane. The calculation of hcurvature(t) is describedbelow.

Let:

-   -   d2x(t), d2y(t), and d2z(t) be the second derivatives of the        trajectory curve with respect to position along the curve at        position t,    -   a=dx(t)*dx(t)+dy(t)*dy(t), and    -   sqrt(x) be a function that returns the square root of x.        Then, hcurvature(t)=(dx(t)*d2y(t)−dy*d2x(t))/sqrt(a*a*a).

At optional step 910, the processor evaluates the vertical curvature ofthe interpolating curve at each roadway trajectory point and at optionalstep 912 the processor populates the corresponding roadway attribute;methods for calculating the vertical curvature will be apparent to oneskilled in the art, now informed by the present disclosure, and are notdescribed further. After step 912 (or after step 908 if steps 910 and912 are omitted), the method 900 ends.

The exemplary logic flow for the method 900 first determines the headingfor all roadway trajectory points and then determines the horizontalcurvature for all roadway trajectory points (and then optionally thevertical curvature for all roadway trajectory points). In otherembodiments these steps may be performed in a different order or inparallel, and/or the heading, horizontal curvature (and optionallyvertical curvature) may be populated for each roadway trajectory pointbefore proceeding to the next roadway trajectory point.

As noted above, at step 704B the processor populates road surfacegeometric roadway attributes. Examples of road surface geometric roadwayattributes for particular roadway trajectory points in the interpolatedpoint sequence include road surface height, road surfacealong-trajectory slope, and road surface cross-trajectory slope. Thepredefined characteristics associated with these roadway attributes areevaluated from the coordinates of a height-filtered set of point cloudpoints within a specified horizontal distance of the point sequencepoint (height-filtering is described in the following paragraph). So,for example, road surface height would be evaluated from the averageheight coordinate of the height-filtered set of point cloud points, androad surface along-trajectory slope and cross-trajectory slope would beevaluated from the upward road surface normal vector that is calculatedfrom the coordinates of the height-filtered set of point cloud points(as explained further below). The upward road surface normal vector is avector pointing away from the Earth and normal to a notional plane thatis locally tangent to the road surface at the horizontal position of therelevant roadway trajectory point.

In some cases, the set of point cloud points within a specifiedhorizontal distance of point sequence points may include point cloudpoints that do not sample the road surface. For example, point cloudpoints that sample an overhead surface where the road passes under anoverpass or is in a tunnel, points that sample a vehicle on the roadway,“noise” or “air” point cloud points that result from imperfections inthe LIDAR sensing and do not sample any physical surface. A heightfiltering technique may be applied to exclude such point cloud pointsfrom the evaluation of road surface geometric roadway attributes.

Reference is now made to FIG. 10, which schematically illustrates anexemplary height filtering technique. This technique requires that abenchmark roadway trajectory point 1002B in the interpolated pointsequence 1004 be placed in a location where only point cloud pointssampling the road surface exist in its neighbourhood and that theneighbourhood contains no less than a threshold number of point cloudpoints 1006 (which will depend on the density and noisiness of the pointcloud). The neighbourhood may be conceptualized as a notional cylinder1008 of defined radius r and infinite length whose axis 1010 is parallelwith the z-axis of the locally level Cartesian coordinate frame andintercepts the roadway trajectory point. Ideally, the benchmark roadwaytrajectory point 1002B is the first roadway trajectory point in theinterpolated point sequence 1004. A threshold along-trajectory absoluteslope value s can then be used to filter out point cloud points 1006associated with the next roadway trajectory point 1002 that do notsample the road surface 1012. A threshold along-trajectory absoluteslope value is typically specified manually, and is designed to identifya height differential between nearby point cloud points that is causedby features other than the roadway surface. For example, point cloudpoints representing a vehicle or an overpass will result in a slope farexceeding any expected slope of the roadway surface itself. Point cloudpoints that define slopes exceeding the along-trajectory absolute slopevalue can then be excluded. For any roadway trajectory point in theinterpolated point sequence, a maximum and minimum height interval [ho .. . h1] is calculated from the threshold along-trajectory absolute slopevalue s, the road surface height value for the closest preceding roadwaytrajectory point 1002 that has a height-filtered neighbourhood with atleast the threshold number of point cloud points 1006, and the curvedistance between the two roadway trajectory points 1002. The point cloudpoints in the neighbourhood set of the current roadway trajectory point1002 that are outside of the maximum and minimum height interval, asillustrated by point cloud points 1006R, are removed from the set.

If a particular roadway trajectory point has too few point cloud pointsremaining in its neighbourhood after application of the filter, the roadsurface geometric roadway attributes may be populated by interpolatingbetween nearby roadway trajectory points. For example, a roadwaytrajectory point may correspond to a position in the point cloud wherethe road surface is occluded from the LIDAR sensor, such as by anothervehicle. For any roadway trajectory point whose height-filteredneighbourhood of point cloud points contains fewer than the thresholdnumber of point cloud points, the road surface geometric roadwayattributes are populated by interpolating the attribute values for thenearest preceding and successive roadway trajectory points whoseheight-filtered neighbourhood of point cloud points contains at leastthe threshold number of point cloud points.

Reference is now made to FIG. 11, which is a flow chart 1100 showing anexemplary elaboration of step 704B, that is, population of road surfacegeometric roadway attributes. The exemplary method 1100, with thepossible exception of step 1102, is carried out by a processor of a dataprocessing system.

At step 1102, the height filter is initialized by selecting thebenchmark roadway trajectory point and setting the thresholdalong-trajectory absolute slope value; one or both of these may be donemanually. At step 1104, the processor calculates the road surface heightas the average height coordinate of the height-filtered set of pointcloud points, and then assigns this attribute value to the correspondingroad surface attribute. The processor then proceeds to step 1106 tocalculate the upwards road surface unit normal vector.

The upwards road surface unit normal vector can be estimated from aheight-filtered neighbourhood of point cloud points with one of severalalgorithms. Exemplary algorithms are taught by Rusu, Rad Bogdan,“Semantic 3D Object Maps for Everyday Manipulation in Human LivingEnvironments,” doctoral dissertation, Computer Science Department,Technische Universitaet Muenchen, Germany, October 2009, the teachingsof which are hereby incorporated by reference. This unit normal vectorwill have x, y, and z components nx, ny, and nz, respectively, in theCartesian coordinate frame.

Let:

-   -   heading(t) be calculated as described above;    -   tp be the position of the roadway trajectory point (measured        parametrically along the interpolating curve to conform to the        variable heading(t))    -   deg_to_rad=pi/180.0, the factor that converts degrees to        radians,    -   rad_to_deg=180.0/pi, the factor that converts radians to        degrees,    -   r=heading(tp)*deg_to_rad,    -   cos (a) and sin (a) be functions that return the cosine and        sine, respectively, of radian angle a, and    -   atan2(x, y) be a function that returns the angle in radians        x-axis of a plane and the point given by the coordinates (x, y)        on it. The angle is positive for counter-clockwise angles (upper        half-plane, y>0), and negative for clockwise angles (lower        half-plane, y<0).

Then, the components of the unit normal vector in the starboard andforward directions (relative to driving direction) are, respectively,rnx=cos (r)*nx−sin (r)*ny and rny=sin (r)*nx+cos (r)*ny.

The processor then proceeds to step 1108 to calculate the road surfacealong-trajectory slope and then to step 1110 to calculate the roadsurface cross-trajectory slope; steps 1108 and 1110 may be equivalentlycarried out in the reverse order or in parallel. At each of steps 1108and 1110, the processor also assigns the respective calculated attributevalue to the corresponding road surface attribute. The road surfacealong-trajectory slope in degrees (positive if climbing, negative ifdescending) is surfasl=rad_to_deg*−atan2(rny, nz) and the road surfacecross-trajectory slope in degrees (positive if banking to starboard,negative if banking to port) is sufxsl=rad_to_deg*atan2(rnx, nz).

After step 1110, the processor proceeds to step 1112 to check if thereare more roadway trajectory points whose road surface geometric roadwayattributes have yet to be examined. If there are no more roadwaytrajectory points to examine (a “no” at step 1112) the processorproceeds to step 1122, described below; if there are more roadwaytrajectory points to examine (a “yes” at step 1112), the processorproceeds to step 1114 to index to the next roadway trajectory point.

After step 1114, at step 1116 the processor applies the height filter tothe neighborhood of point cloud points for the current roadwaytrajectory point, and then proceeds to step 1118 to test whether thefiltered neighborhood of point cloud points has at least the thresholdnumber of point cloud points. It is noted here that steps 1116 (applyingthe height filter) and 1118 (testing whether the filtered neighborhoodof point cloud points has at least the threshold number of point cloudpoints) are not applied to the benchmark roadway trajectory pointselected at step 1102 (initializing the height filter) since these stepswere already at least implicitly performed in selection of the benchmarkroadway trajectory point.

If the processor determines at step 1118 that the filtered neighborhoodof point cloud points has at least the threshold number of point cloudpoints (a “yes” at step 1108), the processor then proceeds to steps1104, 1106 and 1108 for the current roadway trajectory point; exemplaryimplementations of these steps are described above.

If the processor determines at step 1118 that the filtered neighborhoodof point cloud points does not have at least the threshold number ofpoint cloud points (a “no” at step 1108), the processor then proceeds tostep 1120 where the current roadway trajectory point is marked to haveits road surface geometric attributes evaluated by interpolation. Theinterpolation of road surface height, road surface across-trajectoryslope and road surface along-trajectory slope for a roadway trajectorypoint whose neighbourhood has fewer than the threshold number of heightfiltered point cloud points can only be done after those attributes havebeen explicitly evaluated for roadway trajectory points before and afterthat roadway trajectory point. After step 1120, the processor thencontinues to step 1112 to check for more roadway trajectory points toexamine. After a “no” at step 1112, indicating that all the roadwaytrajectory points have been examined and have either been assigned roadsurface geometric attribute values (steps 1104 to 1110) or have beenmarked to have their road surface geometric attribute values assigned byinterpolation (step 1120), the processor continues to step 1122. Step1122 checks if there are any more roadway trajectory points that havebeen marked to have their road surface geometric attribute valuesassigned by interpolation but for which interpolation has not yet beenperformed. Responsive to a “yes” determination at step 1122, theprocessor proceeds to step 1124 to index to the next (or first) markedroadway trajectory point, and then to steps 1126, 1128 and 1130 tointerpolate the road surface height, road surface along-trajectory slopeand road surface cross-trajectory slope, respectively, and assign theinterpolated attribute values to the respective roadway attributes.Steps 1126, 1128 and 1130 may be performed in any order or in parallel.After step 1130, the processor returns to step 1122. Responsive to a“no” determination at step 1122, indicating that interpolation has beenperformed for all roadway trajectory points that were marked to havetheir road surface geometric attribute values assigned by interpolation,the method 1100 ends.

The result of the method 700, which is an elaboration of step 604 of themethod 600 (itself an elaboration of step 108 of the method 100), is anattributed roadway trajectory. An attributed roadway trajectory canprovide sensor-independent roadway data to the control system of aself-driving road vehicle. The term “sensor-independent roadway data”,as used herein, refers to data about a roadway that the control systemof a self-driving road vehicle obtains independently of the vehicle'ssensor array. As such, the attribute values of the attributed roadwaytrajectory points are sensor-independent roadway data.Sensor-independent roadway data may include data of a type that cannotbe discerned by the sensor array of a self-driving road vehicle, such aslegal information (e.g. shoulder drivability). Sensor-independentroadway data may include data of a type that could be obtained from datagathered by the sensor array of a self-driving road vehicle (e.g.along-trajectory slope and/or cross-trajectory slope). However,sensor-independent roadway data of a type that could be gathered by thesensor array of a self-driving road vehicle is distinguished therefromin that it was not actually gathered by the sensor array of thatself-driving road vehicle (even if it was gathered by sensors on adifferent vehicle); sensor-independent roadway data is data about aroadway that is provided to a control system of a self-driving roadvehicle independent of the sensor array of that self-driving roadvehicle.

Sensor-independent roadway data can be made available to the controlsystem of a self-driving road vehicle for positions that are outside ofthe range of the self-driving road vehicle's sensor array. This hasutility in a number of aspects of automated driving.

In one example, information on the horizontal curvature and slopes (roadsurface cross-trajectory slope and road surface along-trajectory slope)on upcoming stretches of road that are beyond sensor range can be usedby the vehicle control system to gradually adjust the vehicle speed sothat the vehicle is travelling at a speed compatible with those valueswhen it reaches those stretches of road. For passenger comfort and thesafety of proximal drivers, a smooth, gradual adjustment of vehiclespeed is preferable to a sudden adjustment. In a highway context, thisis particularly relevant when the vehicle is planning to take an exitonto an interchange.

In another example, information on the road surface along-trajectoryslope of upcoming stretches of road outside of sensor range can be usedto control the vehicle throttle such that constant speed is maintainedfor purposes of fuel economy.

Yet another example is that information on an upcoming speed limitchange at a location beyond sensor range can be used by the vehiclecontrol system to initiate a gradual adjustment of the vehicle's speedso that the vehicle is travelling no faster than the speed limit when itreaches the change. For passenger comfort and the safety of proximaldrivers, a smooth, gradual adjustment of vehicle speed is preferable toa sudden adjustment.

Sensor-independent roadway data is also valuable to the control systemeven for nearby terrestrial positions, especially when thesensor-independent roadway data contains information that cannot beobtained from the onboard sensor array. For example, information on thelocation of the nearest drivable shoulder could be used by the vehiclecontrol system to navigate the vehicle to a safe stop in the event of ablown tire.

Reference is now made to FIG. 12, in which an exemplary self-drivingroad vehicle is indicated generally at 1200. The self-driving roadvehicle 1200 is shown schematically, and various components and systemswhich are known in the art are omitted for brevity and simplicity ofillustration.

The self-driving road vehicle 1200 comprises a body 1204, a locomotionsystem 1208, a steering system 1212, a sensor array 1216 and a controlsystem 1220. The locomotion system 1208 is coupled to the body 1204 foraccelerating, propelling and decelerating the self-driving road vehicle1200 along a roadway, and the steering system 1212 is coupled to thebody 1204 for steering the self-driving road vehicle 1200. The sensorarray 1216 and the control system 1220 are both carried by the body1204; the sensor array 1216 senses driving data and has a sensor range,that is, a range beyond which the sensor array 1216 is unable to resolveuseful data. The sensor array 1216 may include, for example, radar,LIDAR, ultrasonic sensors, cameras (visual and/or thermal), gyroscopes,GPS receivers, inertial measurement systems, accelerometers,magnetometers and thermometers, among others.

The control system 1220 is coupled to the sensor array 1216 forreceiving sensed driving data from the sensor array 1216, and may alsocommunicate with conventional vehicle components such as an onboardcomputer system, speedometer, odometer, engine management system,traction control system, antilock braking system, tire pressure sensors,rain sensors, etc. The control system 1220 is also coupled to thelocomotion system 1208 and the steering system 1212 for controllingthose systems. A data storage module 1224 is coupled to and accessibleby the control system 1220.

The data storage module 1224 stores a plurality of attributed roadwaytrajectories 1228 as described above, at least one of which isassociated with the real-world roadway segment 1232 on which theself-driving road vehicle 1200 is driving. The attributed roadwaytrajectories 1228 may, for example, be organized with other data asconnected components of a road network. Each attributed roadwaytrajectory 1228 comprises at least one ordered series of attributedroadway trajectory points 1236 that are spaced along a curve 1240defined in a terrestrial coordinate frame 1244 and tracking analong-roadway physical feature, in this case a lane divider 1248, of thereal-world roadway segment 1232, which was portrayed in a point cloud.For any arbitrary attributed roadway trajectory point 1236 on the curve1240, that attributed roadway trajectory point 1236 passes apredetermined proximity test for proximity to point cloud pointsdiscriminated as representing part of the along-roadway physical feature(lane divider 1248 in this case). Each attributed roadway trajectorypoint 1236 has, in addition to its position in the terrestrialcoordinate frame 1244, a plurality of roadway attributes 1252 eachhaving respective attribute values 1256 representing characteristics ofthe real-world roadway segment 1232 at a position on the real-worldroadway segment 1232 that is spatially associated with the respectiveattributed roadway trajectory point 1236.

The control system 1220 is configured to obtain, for at least one of theattributed roadway trajectory points 1236, at least one respectiveattribute value 1256 representing sensor-independent roadway data. Thecontrol system 1220 will use the attribute value(s) 1256 to adjustcontrol of the locomotion system 1208 and/or the steering system 1212.In many cases, the control system obtains the attribute value(s) whilethe position of the attributed roadway trajectory point(s) 1236 in theterrestrial coordinate frame 1244 corresponds to a terrestrial positionoutside of the sensor range of the sensor array 1216, and adjustscontrol of the locomotion system 1208 and/or the steering system 1212while the position of the attributed roadway trajectory point(s) 1236 inthe terrestrial coordinate frame 1244 corresponds to a terrestrialposition outside of the sensor range of the sensor array 1216. Somenon-limiting examples of situations in which this control could beapplied are set out above.

Reference is now made to FIG. 13, which is a flow chart showing anexemplary method for controlling a self-driving road vehicle (e.g.self-driving road vehicle 1200 in FIG. 12). At step 1302, a controlsystem (e.g. control system 1220) of the self-driving road vehicleaccesses an attributed roadway trajectory of the type described above(e.g. attributed roadway trajectories 1228). At step 1304, the controlsystem of the self-driving road vehicle obtains, for at least one of theattributed roadway trajectory points (e.g. attributed roadway trajectorypoints 1236), at least one respective attribute value (e.g. attributevalues 1256). As noted above, in many cases the position of theattributed roadway trajectory point(s) in the terrestrial coordinateframe (e.g. terrestrial coordinate frame 1244) corresponds to aterrestrial position outside of the sensor range of the sensor array(e.g. sensor array 1216).

At step 1306, the control system of the self-driving road vehicle usesthe at least one attribute value to adjust control of a locomotionsystem (e.g. locomotion system 1208) and/or a steering system (e.g.steering system 1212) of the self-driving road vehicle; in many casesthis occurs while the position of the attributed roadway trajectorypoint(s) in the terrestrial coordinate frame corresponds to aterrestrial position outside of the sensor range of the sensor array1216.

As can be seen from the above description, the attributed roadwaytrajectories described herein represent significantly more than merelyusing categories to organize, store and transmit information andorganizing information through mathematical correlations. The attributedroadway trajectories are in fact an improvement to the technology ofself-driving road vehicles, as they provide a compact and efficient datastructure for spatially associating sensor-independent roadway data withterrestrial coordinates. This facilitates the ability of the controlsystem of a self-driving road vehicle to use sensor-independent roadwaydata in performing its functions. The attributed roadway trajectory datastructure is also versatile, as it can accommodate a very wide range ofroadway attributes, and existing attributed roadway trajectories can beupdated to add, change or remove roadway attributes. Moreover, theattributed roadway trajectory technology is applied by using aparticular machine, namely a self-driving road vehicle. As such, theattributed roadway trajectory technology is confined to self-drivingroad vehicle applications.

The present technology may be embodied within a system (including acontrol system of a self-driving road vehicle), a method, a computerprogram product or any combination thereof. The computer program productmay include a computer readable storage medium or media having computerreadable program instructions thereon for causing a processor to carryout aspects of the present technology. The computer readable storagemedium can be a tangible device that can retain and store instructionsfor use by an instruction execution device. The computer readablestorage medium may be, for example, but is not limited to, an electronicstorage device, a magnetic storage device, an optical storage device, anelectromagnetic storage device, a semiconductor storage device, or anysuitable combination of the foregoing.

A non-exhaustive list of more specific examples of the computer readablestorage medium includes the following: a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), a staticrandom access memory (SRAM), a portable compact disc read-only memory(CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk,a mechanically encoded device such as punch-cards or raised structuresin a groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present technology may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language or a conventional procedural programminglanguage. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to implement aspects of the present technology.

Aspects of the present technology have been described above withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according to variousembodiments. In this regard, the flowchart and block diagrams in theFigures illustrate the architecture, functionality, and operation ofpossible implementations of systems, methods and computer programproducts according to various embodiments of the present technology. Forinstance, each block in the flowchart or block diagrams may represent amodule, segment, or portion of instructions, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. Some specific examples of the foregoing may havebeen noted above but any such noted examples are not necessarily theonly such examples. It will also be noted that each block of the blockdiagrams and/or flowchart illustration, and combinations of blocks inthe block diagrams and/or flowchart illustration, can be implemented byspecial purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

It also will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks. The computer program instructions may also beloaded onto a computer, other programmable data processing apparatus, orother devices to cause a series of operational steps to be performed onthe computer, other programmable apparatus or other devices to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

An illustrative computer system in respect of which the technologyherein described may be implemented is presented as a block diagram inFIG. 14; computer systems are used in implementing the methods 100 and600 and may also form part of a control system of a self-driving roadvehicle (e.g. the control system 1220 of the self-driving road vehicle1200 shown in FIG. 12). The illustrative computer system is denotedgenerally by reference numeral 1400 and includes a display 1402, one ormore input devices forming a human-machine interface 1404, computer 1406and external devices 1408. Different types of input device(s) anddisplay(s) may be used where the computer system 1400 is configured toform part of a control system of a self-driving road vehicle. Forexample, information may be received from a touchscreen or by speechrecognition. Gesture recognition is also emerging as anotherhuman-machine interface modality, as is tracking of the driver's eyes,both for data input (e.g. look at a button and blink) and also tomonitor the state of the driver (e.g. an assisted-driving road vehiclemay still require the driver to have his or her eyes on the road).

The computer 1406 may contain one or more processors or microprocessors,such as a central processing unit (CPU) 1410. The CPU 1410 performsarithmetic calculations and control functions to execute software storedin an internal memory 1412, preferably random access memory (RAM) and/orread only memory (ROM), and possibly additional memory 1414. Theadditional memory 1414 may include, for example, mass memory storage,hard disk drives, optical disk drives (including CD and DVD drives),magnetic disk drives, magnetic tape drives (including LTO, DLT, DAT andDCC), flash drives, program cartridges and cartridge interfaces such asthose found in video game devices, removable memory chips such as EPROMor PROM, emerging storage media, such as holographic storage, or similarstorage media as known in the art. This additional memory 1414 may bephysically internal to the computer 1406, or external as shown in FIG.14, or both.

The computer system 1400 may also include other similar means forallowing computer programs or other instructions to be loaded. Suchmeans can include, for example, a communications interface 1416 whichallows software and data to be transferred between the computer system1400 and external systems and networks. Examples of communicationsinterface 1416 can include a modem, a network interface such as anEthernet card, a wireless communication interface, or a serial orparallel communications port. Software and data transferred viacommunications interface 1416 are in the form of signals which can beelectronic, acoustic, electromagnetic, optical or other signals capableof being received by communications interface 1416. Multiple interfaces,of course, can be provided on a single computer system 1400. Aself-driving road vehicle may have multiple computing devices thatcommunicate with one another. Traditionally, a controller area network(CAN) bus has been used for data exchange, although this is changing tomore conventional networking. Some vehicles may use wirelesscommunication to receive software updates from a vendor.

Input and output to and from the computer 1406 is administered by theinput/output (I/O) interface 1418. This I/O interface 1418 administerscontrol of the display 1402, human-machine interface 1404, externaldevices 1408 and other such components of the computer system 1400.Where the computer system 1400 forms part of a control system of aself-driving road vehicle, the I/O interface 1418 may also administercommunication with the locomotion system, steering system and sensorarray of a self-driving road vehicle. The computer 1406 also includes agraphical processing unit (GPU) 1420. The latter may also be used forcomputational purposes as an adjunct to, or instead of, the CPU 1410,for mathematical calculations.

The various components of the computer system 1400 are coupled to oneanother either directly or by coupling to suitable buses.

Thus, computer readable program code for implementing aspects of thetechnology described herein may be contained or stored in the memory1412 of the computer 1406, or on a computer usable or computer readablemedium external to the computer 1406, or on any combination thereof.

Finally, the terminology used herein is for the purpose of describingparticular embodiments only and is not intended to be limiting. As usedherein, the singular forms “a”, “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. It is to be understood that where thespecification or the claims refers to a device being “for” doing aparticular thing, that device is adapted to do that particular thing.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription has been presented for purposes of illustration anddescription, but is not intended to be exhaustive or limited to the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope of theclaims. The embodiment was chosen and described in order to best explainthe principles of the technology and the practical application, and toenable others of ordinary skill in the art to understand the technologyfor various embodiments with various modifications as are suited to theparticular use contemplated.

Certain currently preferred embodiments have been described by way ofexample. It will be apparent to persons skilled in the art that a numberof variations and modifications can be made without departing from thescope of the claims. In construing the claims, it is to be understoodthat the use of a computer to implement the embodiments describedherein, and in particular that an attributed roadway trajectory be usedin a control system of a self-driving road vehicle, is essential.

1. A method for pre-processing roadway data, comprising: receiving, in aprocessor of a data processing system, at least one ordered series ofroadway trajectory points, wherein for each ordered series of roadwaytrajectory points: the roadway trajectory points are spaced along acurve; the curve being defined in a terrestrial coordinate frame andtracking an along-roadway physical feature of a real-world roadwaysegment portrayed in a point cloud; each roadway trajectory point havinga position in the terrestrial coordinate frame; for any arbitraryroadway trajectory point on the curve, that roadway trajectory pointpasses a predetermined proximity test for proximity to point cloudpoints discriminated as representing part of the along-roadway physicalfeature; each roadway trajectory point having at least one roadwayattribute; in the processor of the data processing system, for eachroadway trajectory point in the ordered series of roadway trajectorypoints: evaluating at least one predefined characteristic of thereal-world roadway segment at a position on the real-world roadwaysegment that is spatially associated with the roadway trajectory pointto generate an attribute value for each predefined characteristic; andassigning each attribute value to the corresponding roadway attribute ofthe roadway trajectory point; to generate an attributed roadwaytrajectory for use in a control system of a self-driving road vehicle.2. The method of claim 1, wherein the roadway trajectory points areuniformly spaced along the curve.
 3. The method of claim 1, wherein: theat least one ordered series of roadway trajectory points comprises aplurality of ordered series of roadway trajectory points; boundariesbetween the ordered series of roadway trajectory points being defined bydiscontinuities between the curve of a preceding one of the series ofroadway trajectory points and the curve of a subsequent one of theseries of roadway points; wherein the discontinuities between the curvesare associated with respective corresponding discontinuities in thealong-roadway physical feature.
 4. The method of claim 1, wherein the atleast one roadway attribute comprises at least one non-geometric roadwayattribute.
 5. The method of claim 4, wherein: evaluating at least onepredefined characteristic of the real-world roadway segment at aposition on the real-world roadway segment that is spatially associatedwith the roadway trajectory point to generate an attribute value foreach predefined characteristic; and assigning each attribute value tothe corresponding roadway attribute of the roadway trajectory point;comprises: receiving a set of landmark points; each landmark pointhaving a position in the terrestrial coordinate frame; each landmarkpoint having at least one landmark attribute characterizing thatlandmark point; for each landmark point: locating a closest roadwaytrajectory point that is horizontally closest to that landmark point;testing a horizontal distance between that landmark point and theclosest roadway trajectory point against a maximum distance threshold;and responsive to the horizontal distance being less than or equal tothe maximum distance threshold, assigning at least one attribute valueto a corresponding at least one roadway attribute of the closest roadwaytrajectory point according to at least one corresponding landmarkattribute.
 6. The method of claim 5, where in the set of landmark pointscomprises a plurality of landmark points of different landmark types. 7.The method of claim 1, wherein the at least one roadway attributecomprises at least one geometric roadway attribute.
 8. The method ofclaim 7, wherein the at least one geometric roadway attribute comprisesat least one road surface geometric roadway attribute.
 9. The method ofclaim 8, wherein the at least one road surface geometric roadwayattribute comprises at least one of road surface along-trajectory slopeand cross-trajectory slope.
 10. The method of claim 7, wherein the atleast one geometric roadway attribute comprises at least one trajectorycurve geometric roadway attribute.
 11. The method of claim 10, whereinthe at least one trajectory curve geometric roadway attribute comprisesat least one of heading, horizontal curvature, and vertical curvature.12. A self-driving road vehicle, comprising: a body; a locomotion systemcoupled to the body for accelerating, propelling and decelerating thevehicle along a roadway; a steering system coupled to the body forsteering the vehicle; a sensor array carried by the body for sensingdriving data, the sensor array having a sensor range; a control systemcarried by the body, wherein: the control system is coupled to thesensor array for receiving sensed driving data from the sensor array;the control system is coupled to the locomotion system for controllingthe locomotion system; and the control system is coupled to the steeringsystem for controlling the steering system; a data storage modulecoupled to and accessible by the control system, the data storage modulestoring at least one attributed roadway trajectory; each attributedroadway trajectory comprising: at least one ordered series of attributedroadway trajectory points, wherein for each ordered series of attributedroadway trajectory points: the attributed roadway trajectory points arespaced along a curve; the curve is defined in a terrestrial coordinateframe and tracks an along-roadway physical feature of a real-worldroadway segment portrayed in a point cloud; for any arbitrary attributedroadway trajectory point on the curve, that attributed roadwaytrajectory point passes a predetermined proximity test for proximity topoint cloud points discriminated as representing part of thealong-roadway physical feature; each attributed roadway trajectory pointhas, in addition to its position in the terrestrial coordinate frame, atleast one roadway attribute having a respective attribute value; eachattribute value representing a characteristic of the real-world roadwaysegment at a position on the real-world roadway segment that isspatially associated with the attributed roadway trajectory point;wherein the control system is configured to: obtain, for at least one ofthe attributed roadway trajectory points, at least one respectiveattribute value representing sensor-independent roadway data; and usethe at least one attribute value to adjust control of at least one ofthe locomotion system and the steering system.
 13. The method of claim12, wherein the control system is configured to: obtain the at least onerespective attribute value representing sensor-independent roadway datawhile the position in the terrestrial coordinate frame of the respectiveat least one of the attributed roadway trajectory points corresponds toa terrestrial position outside of the sensor range of the sensor array;and use the at least one attribute value to adjust control of at leastone of the locomotion system and the steering system while the positionin the terrestrial coordinate frame of the respective at least one ofthe attributed roadway trajectory points corresponds to a terrestrialposition outside of the sensor range of the sensor array.
 14. Theself-driving road vehicle of claim 12, wherein: the at least one orderedseries of attributed roadway trajectory points comprises a plurality ofordered series of attributed roadway trajectory points; boundariesbetween the ordered series of attributed roadway trajectory points beingdefined by discontinuities between the curve of a preceding one of theseries of attributed roadway trajectory points and the curve of asubsequent one of the series of roadway points; wherein thediscontinuities between the curves are associated with respectivecorresponding discontinuities in the along-roadway physical feature. 15.The self-driving road vehicle of claim 12, wherein the attributedroadway trajectory points are uniformly spaced along the curve.
 16. Theself-driving road vehicle of claim 12, wherein the at least one roadwayattribute comprises at least one non-geometric roadway attribute. 17.The self-driving road vehicle of claim 12, wherein the at least oneroadway attribute comprises at least one geometric roadway attribute.18. The self-driving road vehicle of claim 17, wherein the at least onegeometric roadway attribute comprises at least one road surfacegeometric roadway attribute.
 19. The self-driving road vehicle of claim17, wherein the at least one geometric roadway attribute comprises atleast one trajectory curve geometric roadway attribute.
 20. A method forcontrolling a self-driving road vehicle, comprising: a control system ofthe self-driving road vehicle accessing an attributed roadwaytrajectory, the attributed roadway trajectory comprising: at least oneordered series of attributed roadway trajectory points, wherein for eachordered series of attributed roadway trajectory points: the attributedroadway trajectory points are spaced along a curve; the curve is definedin a terrestrial coordinate frame and tracks an along-roadway physicalfeature of a real-world roadway segment portrayed in a point cloud; forany arbitrary attributed roadway trajectory point on the curve, thatattributed roadway trajectory point passes a predetermined proximitytest for proximity to point cloud points discriminated as representingpart of the along-roadway physical feature; each attributed roadwaytrajectory point has, in addition to its position in the terrestrialcoordinate frame, at least one roadway attribute having a respectiveattribute value; each attribute value representing a characteristic ofthe real-world roadway segment at a position on the real-world roadwaysegment that is spatially associated with the attributed roadwaytrajectory point; the control system of the self-driving road vehicleobtaining, for at least one of the attributed roadway trajectory points,at least one respective attribute value representing sensor-independentroadway data; and the control system of the self-driving road vehicleusing the at least one attribute value to adjust control of at least oneof the locomotion system and the steering system.
 21. (canceled) 22.(canceled)
 23. (canceled)
 24. (canceled)
 25. (canceled)
 26. (canceled)27. (canceled)